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Evaluating the Performance of Iterative Proportional Fitting for Spatial Microsimulation: New Tests for an Established Technique

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Abstract

Iterative Proportional Fitting (IPF), also known as biproportional fitting, ‘raking’ or the RAS algorithm, is an established procedure used in a variety of applications across the social sciences. Primary amongst these for urban modelling has been its use in static spatial microsimulation to generate small area microdata — individual level data allocated to administrative zones. The technique is mature, widely used and relatively straight-forward. Although IPF is well described mathematically, accessible examples of the algorithm written in modern programming languages are rare. There is a tendency for researchers to ‘start from scratch’, resulting in a variety of ad hoc implementations and little evidence about the relative merits of differing approaches. These knowledge gaps mean that answers to certain methodological questions must be guessed: How can ‘empty cells’ be identified and how do they influence model fit? Can IPF be made more computationally efficient? This paper tackles these questions and more using a systematic methodology with publicly available code and data. The results demonstrate the sensitivity of the results to initial conditions, notably the presence of ‘empty cells’, and the dramatic impact of software decisions on computational efficiency. The paper concludes by proposing an agenda for robust and transparent future tests in the field.

Suggested Citation

  • Robin Lovelace & Mark Birkin & Dimitris Ballas & Eveline van Leeuwen, 2015. "Evaluating the Performance of Iterative Proportional Fitting for Spatial Microsimulation: New Tests for an Established Technique," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-21.
    • Handle: RePEc:jas:jasssj:2014-122-3
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    References listed on IDEAS

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    Cited by:

    1. Hynes, Stephen & O'Donoghue, Cathal, 2019. "Estimating the value of achieving good ecological status across Irish water catchments using value transfer," Working Papers 309538, National University of Ireland, Galway, Socio-Economic Marine Research Unit.
    2. Kate A Timmins & Kimberley L Edwards, 2016. "Validation of Spatial Microsimulation Models: a Proposal to Adopt the Bland-Altman Method," International Journal of Microsimulation, International Microsimulation Association, vol. 9(2), pages 106-122.
    3. Yong Jee KIM & Brigitte WALDORF & Juan SESMERO, 2020. "Relocation, Retreat, and the Rising Sea Level: A Simulation of Aggregate Outcomes in Escambia County, Florida," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 51, pages 31-43.
    4. Spooner, Fiona & Abrams, Jesse F. & Morrissey, Karyn & Shaddick, Gavin & Batty, Michael & Milton, Richard & Dennett, Adam & Lomax, Nik & Malleson, Nick & Nelissen, Natalie & Coleman, Alex & Nur, Jamil, 2021. "A dynamic microsimulation model for epidemics," Social Science & Medicine, Elsevier, vol. 291(C).
    5. Kelli Francis-Staite, 2022. "Internal multi-portfolio rebalancing processes: Linking resource allocation models and biproportional matrix techniques to portfolio management," Papers 2201.06183, arXiv.org.

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